UQSay #89

The eighty-ninth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, October 30, 2025.

2–3 PM — Edgar Jaber (EDF R&D, Centre Borelli, LISN)

A Bayesian methodology for hybrid degradation prognostics

Degradation prognostics of industrial assets involves estimating their remaining useful life (RUL) by projecting current health indicators and operating conditions while quantifying associated uncertainties. These prognostics are central to the development and deployment of digital twins, which aim to provide insights into the evolving state of complex systems. Traditionally, RUL estimation relies on physics-based simulations or data-driven models. While both have their merits, they can prove inadequate when simulation runtimes are prohibitive or when degradation data is sparse, common challenges in digital twin implementations for critical industrial infrastructure.

To address this problem, we developed an offline modular data assimilation approach. Firstly, a Bayesian model updating strategy combines kernel-based sensitivity analysis to identify and rank the time-varying influence of the model’s input variables, with a tailored inference scheme that accounts for the heterogeneity of available data. Posterior distributions are sampled using MCMC techniques, while the method mitigates the curse of dimensionality by iteratively updating the marginals of influential input variables under an independence assumption. Posterior informativeness is quantified through the Kullback–Leibler divergence, comparing updated distributions to their priors. Secondly, the full state distribution is updated with the help of an ensemble Kalman smoothing step, further reducing the posterior uncertainty.

After detailing the methodology, I will illustrate how this approach enhances the fidelity of RUL predictions and reduces uncertainty in a clogging prognostics use case for digital twins of steam generators in nuclear power plants.

References:

• Fusion of heterogeneous data for robust degradation prognostics, 2025 - github

Joint work with Emmanuel Remy (EDF R&D) & Vincent Chabridon (EDF R&D) & Mathilde Mougeot (ENS Paris-Saclay) & Didier Lucor (LISN).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay #88

The eighty-eighth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, October 16, 2025.

2–3 PM — Virginie Ehrlacher (CERMICS, Ecole Nationale des Ponts et Chaussées) — [slides]

Marginal-constrained modified Wasserstein barycenters for Gaussian distributions and Gaussian mixtures

The aim of this talk is to present some modified Wasserstein barycenters for probability measures defined on cartesian product sets which satisfy given marginal constraints. We focus on the specific case of Gaussian and Gaussian mixture distributions, as the proposed approach strongly relies on new results about properties of geometric means of covariance matrices. In the case of Gaussian distributions, the marginal-constrained modified Wasserstein barycenters can be analytically computed, while for Gaussian mixtures, computing the marginal-constrained barycenter consists in a postprocessing of the Gaussian mixture Wasserstein barycenter. In both cases, we provide numerical simulations illustrating the difference between Wasserstein barycenters and modified marginal-constrained Wasserstein barycenters. We moreover provide several test cases where the marginal-constrained Wasserstein barycenters interpolate better than regular Wasserstein barycenters, showcasing the practical interest of the proposed approach. As a by-product, we prove new results concerning marginal-preserving Wasserstein barycenters. Indeed, Wasserstein barycenters do not preserve marginals in general. In this work, as a consequence of the derived properties on the geometric mean of covariance matrices, we obtain sufficient and necessary conditions for the Wasserstein barycenter between two Gaussian distributions to preserve marginals, and provide necessary conditions in the case of more than two Gaussians.

References:

• Marginal-constrained modified Wasserstein barycenters for Gaussian distributions and Gaussian mixtures, 2025

Joint work with Maxime Daléry (LMB - Université Franche-Comté) & Geneviève Dusson (LMB - Université Franche-Comté).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay #87

The eighty-seventh UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, September 18, 2025.

2–3 PM — Pamphile Roy (LUT Business School, LUT University - Consulting Manao GmbH, Austria) — [slides]

A novel way of visualizing causal uncertainty

Global sensitivity analysis (GSA) is crucial for understanding model behavior and informing decision-making. However, its adoption is hindered by methodological complexity, implementation challenges, and high computational costs. To address these issues, we developed Simulation Decomposition (SimDec), a hybrid approach that simplifies GSA through efficient computation of variance-based sensitivity indices and intelligent visualization techniques. SimDec is made accessible to practitioners of any background via a no-code web dashboard. The latest enhancement to the SimDec dashboard includes two-output graphs, which allow users to visualize relationships between two model outputs alongside their marginal distributions. This feature is demonstrated through a case study on optimizing a heat exchanger in a nuclear reactor, examining the relationship between the levelized cost of heat and mechanical design characteristics. By providing an intuitive, no-code interface, SimDec democratizes GSA, making it accessible to users with limited mathematical training. This work was presented at the SAMO 2025 conference, highlighting the potential of SimDec to transform how sensitivity analysis is conducted across various fields.

References:

• Uncovering heterogeneous effects in computational models for sustainable decision-making, Environmental Modelling & Software 2024
• Simulation Decomposition in Python, Journal of Open Source Software 2024 - [SimDec dashboard]
• Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models, Journal of Environmental Informatics Letters 2025

Joint work with Mariia Kozlova (LUT University) & Andrea Saltelli (UPF Barcelona School of Management) & Julian Scott Yeomans (York University).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay #86

The eighty-sixth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, May 15, 2025.

2–3 PM — Johanna Ziegel (ETH Zürich) — [slides]

(Conformal) isotonic distributional regression

Isotonic distributional regression (IDR) is a nonparametric distributional regression approach under a monotonicity constraint. It has found application as a generic method for uncertainty quantification, in statistical postprocessing of weather forecasts, and in distributional single index models. IDR has favorable in-sample calibration and optimality properties, which allow to conformalize it and obtain out-of-sample online guarantees.

References:

• Isotonic Distributional Regression, JRSS Series B 2023
• Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output, SIAM Review 2024
• In-sample calibration yields conformal calibration guarantees, 2025

Joint work with S. Allen (KIT) & G. Gavrilopoulos & A. Henzi (ETH Zürich) & T. Gneiting & E-M. Walz (HITS-KIT).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay #85

The eighty-fifth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, April 3, 2025.

2–3 PM — Bilel Bensaid (Toulouse School of Economics) — [slides]

New insights in neural networks optimization: Lyapunov stability and splitting schemes

These recent years, a great number of algorithms have been developed to optimize neural networks parameters (p-GD, clipping GD, Momentum, RMSProp, Adam, ...) but they need an accurate tuning to be stable and efficient. To get rid of the long and experimental step of GridSearch, we are looking for adaptive optimizers that come with guarantees. By analysing the stability of these algorithms, a general methodology to adapt the learning rate is suggested (generalization of the Armijo rule) for any deep learning optimizers, relating "robust" optimizers to preserving discretization schemes. Convergence and complexity of these methods are discussed leading to acceleration results, promoting the use of adaptive learning rate strategies for Analytic and Recurrent Neural Networks.

Finally, this study is extended to the mini-batch setting, revealing the link between mini-batch optimization and splitting operator methods. In a nutshell, this work comes up with deep relations between neural network training and classical issues in the numerical analysis of differential equations. .

References:

• Deterministic Neural Networks Optimization from a Continuous and Energy Point of View, J. Scientific Computing 2023
• An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence, 2024
• Convergence of the Iterates for Momentum and RMSProp for Local Smooth Functions: Adaptation is the Key, 2024

Joint work with G. Poette (CEA DAM, CESTA - ENSEIRB-Matmeca) & R. Turpault (IMB - ENSEIRB-Matmeca).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay # 84

The eighty-fourth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, March 6, 2025.

2–3 PM — Elena Di Bernardino (Laboratoire Jean Alexandre Dieudonné, Université Côte d'Azur) — [slides]

Curvature measures for random excursion sets: theoretical and computational developments

The excursion set of a smooth random field carries relevant information in its various geometric measures. Geometric properties of these exceedance regions above a given level provide meaningful theoretical and statistical characterizations for random fields defined on Euclidean domains. Many theoretical results have been obtained for excursions of Gaussian processes and include expected values of the so-called Lipschitz-Killing curvatures (LKCs), such as the area, perimeter and Euler characteristic in two-dimensional Euclidean space. In this talk we will describe a recent series of theoretical and computational contributions in this field. Our aim is to provide answers to questions like:

- How the geometric measures of an excursion set can be inferred from a discrete sample of the excursion set;

- How these measures can be related back to the distributional properties of the random field from which the excursion set was obtained;

- How the excursion set geometry can be used to infer the extremal behavior of random fields.

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.

UQSay #83

The eighty-third UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, February 13, 2025.

2–3 PM — Margot Hérin (LIP6, Sorbonne University) — [slides]

Algorithms for learning capacity-based preference models

Preference models from Decision Theory are used to describe, explain, or predict human behavior in evaluation or decision-making tasks. Beyond this descriptive role, a key feature of these models is their ability to guarantee normative properties, ensuring the internal consistency of the modeled value system and the resulting decisions. Hence, they can also be used to assist individuals in making a relevant choice based on their preferences or provide machines with the ability to autonomously yet controllably make sophisticated decisions in complex environments, involving multi-criteria or collective decision-making, or decision-making under uncertainty.

In this talk, we consider aggregation functions weighted by a non-additive set function (called capacity), such as multilinear utilities or Choquet integrals. The non-additivity of the capacity makes it possible to model criteria interactions, leaving room for a diversity of attitudes in criteria aggregation. However, allowing for these interactions dramatically increases the complexity of the preference learning task and may prevent the model from being interpretable, due to the combinatorial nature of the possible interactions.

We address this challenge by learning a sparse Möbius transform of the capacity, where the few non-zero Möbius masses indicate the significant positive or negative synergies between criteria. Specifically, we propose a learning method based on iterative reweighted least squares (IRLS) for sparse recovery, and dualization to improve scalability, making it possible to handle aggregation problems involving more than 20 criteria. We also present an online learning algorithm based on regularized dual averaging (RDA), designed for decision-making contexts where preference examples become available sequentially, but also well suited to handle large-scale preference databases (large number of preferences or criteria examples). In addition, the inclusion of normative constraints on the capacity (e.g., monotonicity, supermodularity) is made possible by combining RDA with the method of alternating direction multipliers (ADMM).

References:

• Learning Preference Models with Sparse Interactions of Criteria, IJCAI 2023
• Online Learning of Capacity-Based Preference Models, IJCAI 2024

Joint work with P. Perny (Sorbonne University) & N. Sokolovska (Sorbonne University).

Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Vincent Chabridon (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Clément Gauchy (CEA), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Sébastien Petit (LNE), Emmanuel Vazquez (L2S), Xujia Zhu (L2S).

Coordinators: Sidonie Lefebvre (ONERA) & Xujia Zhu (L2S)

Practical details: the seminar will be held online using Microsoft Teams.

If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).

You will find the link to the seminar on the "General" UQSay channel on Teams, approximately 15 minutes before the beginning.

The technical side of things: you can use Teams either directly from your web browser or using the "fat client", which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.